To provide convenient access to epidemiological data on the coronavirus outbreak, we developed an R package, nCov2019 (https://github.com/yulab-smu/nCov2019). Besides detailed basis statistics, it also includes information about vaccine development and therapeutics candidates. We redesigned the function plot() for geographic maps visualization and provided a interactive shiny app. These analytics tools could be useful in informing the public and studying how this and similar viruses spread in populous countries.
Our R package is designed for both command line and dashboard interaction analysis, As show in diagram, while dashboard() is the main entry for the GUI explore part, the query() is the main function used in CLI explore part, 5 types of data were contain in its return result. Result type were explain in Statistic query part.
Installation
To start off, users could utilize the ‘remotes’ package to install it directly from GitHub by running the following in R:
remotes::install_github("yulab-smu/nCov2019", dependencies = TRUE)Statistic query
Data query is simple as one command:
library("nCov2019")
res <- query()## Querying the latest data...## last update: 2021-04-02## Querying the global data...## Gloabl total 130174617 cases; and 2840184 deaths
## Gloabl total affect country or areas: 221
## Gloabl total recovered cases: 7609
## last update: 2021-04-02## Querying the historical data...## Querying the vaccine data...## Total Candidates Programs : 51## Querying the therapeutics data...## Total Candidates Programs : 54## Query finish, each time you can launch query() to reflash the dataThis may take seconds to few minutes, which depend on the users’ network connection, if the user connection is broken, a local stored version data will be used for demo.
The result returned by query() function will contains 5 types of statistic:
names(res)## [1] "latest" "global" "historical" "vaccine" "therapeutics"globalThe global overall summary statistic
latestThe global latest statistic for all countrieshistoricalThe historical statistic for all countriesvaccineThe current vaccine development progresstherapeuticsThe current therapeutics development progress
The query() only need to be performed once in a session, print each of statistic objects, users could get their update time. And for the vaccine and therapeutics query results, print them will return the candidates number.
Global data
The query result of global status will contain a data frame with 21 types of statistic, which have detail explanation on the bottom of this documents. And summary(x) will return overview of global status.
x = res$global
x$affectedCountries # total affected countries## [1] 221summary(x)## Gloabl total 130174617 cases; and 2840184 deaths
## Gloabl total affect country or areas: 221
## Gloabl total recovered cases: 7609
## last update: 2021-04-02Latest data
Here is the example for operating latest data. once again, all data have queried and store in res.
x = res$latestAnd then print(x) will return the update time for the latest data
print(x) # check update time## last update: 2021-04-02To subset latest data could be easily done by using [. x["Global"] or x["global"] will return the data frame for all countries but users could determine a specific country, such as:
head(x["Global"]) # return all global countries.## country cases deaths recovered active todayCases todayDeaths
## 1 Brazil 12842717 325559 11239099 1278059 89459 3673
## 2 India 12302110 163428 11522884 615798 81441 468
## 3 USA 31244639 566611 23754391 6923637 76786 952
## 4 France 4695082 95976 296166 4302940 50659 314
## 5 Turkey 3357988 31713 3035043 291232 40806 176
## 6 Poland 2356970 53665 1882179 421126 35251 621
## todayRecovered population tests updated
## 1 69162 213691728 28600000 2021-04-02
## 2 50390 1390160589 244798621 2021-04-02
## 3 80840 332457943 405032316 2021-04-02
## 4 1528 65381957 64823784 2021-04-02
## 5 20817 85018143 38821795 2021-04-02
## 6 20254 37815450 12145470 2021-04-02x[c("USA","India")] # return only for USA and India ## country cases deaths recovered active todayCases todayDeaths
## 2 India 12302110 163428 11522884 615798 81441 468
## 3 USA 31244639 566611 23754391 6923637 76786 952
## todayRecovered population tests updated
## 2 50390 1390160589 244798621 2021-04-02
## 3 80840 332457943 405032316 2021-04-02The data is order by “todayCases” column, users could sort them by other order.
df = x["Global"]
head(df[order(df$cases, decreasing = T),]) ## country cases deaths recovered active todayCases todayDeaths
## 3 USA 31244639 566611 23754391 6923637 76786 952
## 1 Brazil 12842717 325559 11239099 1278059 89459 3673
## 2 India 12302110 163428 11522884 615798 81441 468
## 4 France 4695082 95976 296166 4302940 50659 314
## 16 Russia 4554264 99233 4176419 278612 9169 383
## 32 UK 4350266 126764 3860174 363328 4478 51
## todayRecovered population tests updated
## 3 80840 332457943 405032316 2021-04-02
## 1 69162 213691728 28600000 2021-04-02
## 2 50390 1390160589 244798621 2021-04-02
## 4 1528 65381957 64823784 2021-04-02
## 16 10247 145981477 120600000 2021-04-02
## 32 12823 68153448 127546869 2021-04-02As for the latest data, it provides 11 types of main information by default, but 12 more statistic type are provided in the “latest$detail”, they also have corresponding explanation on the bottom.
x = res$latest
head(x$detail) # more detail data ## updated country countryInfo._id countryInfo.iso2 countryInfo.iso3
## 1 2021-04-02 Brazil 76 BR BRA
## 2 2021-04-02 India 356 IN IND
## 3 2021-04-02 USA 840 US USA
## 4 2021-04-02 France 250 FR FRA
## 5 2021-04-02 Turkey 792 TR TUR
## 6 2021-04-02 Poland 616 PL POL
## countryInfo.lat countryInfo.long countryInfo.flag
## 1 -10 -55 https://disease.sh/assets/img/flags/br.png
## 2 20 77 https://disease.sh/assets/img/flags/in.png
## 3 38 -97 https://disease.sh/assets/img/flags/us.png
## 4 46 2 https://disease.sh/assets/img/flags/fr.png
## 5 39 35 https://disease.sh/assets/img/flags/tr.png
## 6 52 20 https://disease.sh/assets/img/flags/pl.png
## cases todayCases deaths todayDeaths recovered todayRecovered active
## 1 12842717 89459 325559 3673 11239099 69162 1278059
## 2 12302110 81441 163428 468 11522884 50390 615798
## 3 31244639 76786 566611 952 23754391 80840 6923637
## 4 4695082 50659 95976 314 296166 1528 4302940
## 5 3357988 40806 31713 176 3035043 20817 291232
## 6 2356970 35251 53665 621 1882179 20254 421126
## critical casesPerOneMillion deathsPerOneMillion tests testsPerOneMillion
## 1 8318 60099 1523 28600000 133838
## 2 8944 8849 118 244798621 176094
## 3 8727 93981 1704 405032316 1218296
## 4 5109 71810 1468 64823784 991463
## 5 2135 39497 373 38821795 456630
## 6 3143 62328 1419 12145470 321177
## population continent oneCasePerPeople oneDeathPerPeople oneTestPerPeople
## 1 213691728 South America 17 656 7
## 2 1390160589 Asia 113 8506 6
## 3 332457943 North America 11 587 1
## 4 65381957 Europe 14 681 1
## 5 85018143 Asia 25 2681 2
## 6 37815450 Europe 16 705 3
## activePerOneMillion recoveredPerOneMillion criticalPerOneMillion
## 1 5980.85 52594.92 38.93
## 2 442.97 8288.89 6.43
## 3 20825.60 71450.82 26.25
## 4 65812.35 4529.78 78.14
## 5 3425.53 35698.77 25.11
## 6 11136.35 49772.75 83.11Historical data
Historical data is useful in retrospective analysis or to establish predictive models, the operation is similar as latest data, user could get the data frame for all countries or some specific countries within c() vector, such as head(Z[c(country1,country2,country3)])
Z = res$historical
print(Z) # update time## last update: 2021-03-31head(Z["Global"])## country date cases deaths recovered
## 1 Afghanistan 2020-01-22 0 0 0
## 193 Afghanistan 2020-01-23 0 0 0
## 385 Afghanistan 2020-01-24 0 0 0
## 577 Afghanistan 2020-01-25 0 0 0
## 769 Afghanistan 2020-01-26 0 0 0
## 961 Afghanistan 2020-01-27 0 0 0head(Z[c("China","UK","USA")]) ## country date cases deaths recovered
## 37 China 2020-01-22 548 17 28
## 229 China 2020-01-23 643 18 30
## 421 China 2020-01-24 920 26 36
## 613 China 2020-01-25 1406 42 39
## 805 China 2020-01-26 2075 56 49
## 997 China 2020-01-27 2877 82 58For the following countries, we provide detail province data, which can be obtained in a similar way but within [ operation: head(Z[country,province])
AustraliaCanadaChinaDenmarkFranceNetherlands
head(Z['China','hubei'])## country province date cases deaths recovered
## 34 China hubei 2020-01-22 444 17 28
## 119 China hubei 2020-01-23 444 17 28
## 204 China hubei 2020-01-24 549 24 31
## 289 China hubei 2020-01-25 761 40 32
## 374 China hubei 2020-01-26 1058 52 42
## 459 China hubei 2020-01-27 1423 76 45For users’ own historical data, we provide a convert() function, users could convert other data into class of nCov2019History data, and then explore in nCov2019:
userowndata <- read.csv("path_to_user_data.csv")
# userowndata, it should contain these 6 column:
# "country","province","date","cases","deaths","recovered"
Z = convert(data=userowndata)
head(Z["Global"])Vaccine and therapeutics data
Users could check for the vaccine or therapeutics developing status. Let x be the vaccine or therapeutics query result, then summary() will return the summary of their trial phase, and x[“all”] or x[“All”] will return the summary information, such as mechanism, trial Phase, institutions and so on. Then the detail background info will return with provided id, for example x[ID=“id3”] or simple as x[“id3”]. The same operation could apply to therapeutics data.
X <- res$vaccine
summary(X)## phase candidates
## 1 Phase 3 10
## 2 Phase 2/3 3
## 3 Phase 2 2
## 4 Phase 1/2 9
## 5 Phase 1 13
## 6 Pre-clinical 14head(X["all"])## id candidate
## 1 id1 BNT162
## 2 id2 mRNA-1273
## 3 id3 Ad5-nCoV
## 4 id4 AZD1222
## 5 id5 CoronaVac
## 6 id6 Covaxin
## mechanism
## 1 mRNA-based vaccine
## 2 mRNA-based vaccine
## 3 Recombinant vaccine (adenovirus type 5 vector)
## 4 Replication-deficient viral vector vaccine (adenovirus from chimpanzees)
## 5 Inactivated vaccine (formalin with alum adjuvant)
## 6 Inactivated vaccine
## sponsors trialPhase
## 1 Pfizer, BioNTech Phase 3
## 2 Moderna Phase 3
## 3 CanSino Biologics Phase 3
## 4 The University of Oxford Phase 3
## 5 Sinovac Phase 3
## 6 Bharat Biotech Phase 3
## institutions
## 1 Multiple study sites in Europe, North America and China
## 2 Kaiser Permanente Washington Health Research Institute
## 3 Tongji Hospital
## 4 The University of Oxford, 
## 5 Sinovac Research and Development Co., Ltd.
## 6# check for the details about the mRNA-based vaccine, id3
X[ID="id3"]## [1] "Background: China’s CanSino Biologics has developed a recombinant novel coronavirus vaccine that incorporates the adenovirus type 5 vector (Ad5) named Ad5-nCoV. Trials: Multiple trials are in various stages of recruitment and completion: - A Phase 1 clinical trial in China of 108 participants between 18 and 60 years old who will receive low, medium, and high doses of Ad5-nCoV is active, but not recruiting (NCT04313127). - A Phase 1 trial in China is evaluating intramuscular vaccination and mucosal vaccination of Ad5-nCoV across two doses (NCT04552366). - A Phase 1/2 trial of up to 696 participants in Canada (NCT04398147). - A Phase 2 double-blind, placebo-controlled trial of up to 508 participants in China (NCT04341389) is active, but not recruiting. - A Phase 2b trial in China evaluating safety and immunogenicity of Ad5-nCoV in participants 6 years and older (NCT04566770). - A Phase 3 trial in Russia of up to 500 participants across multiple study centers (NCT04540419). - A Phase 3 trial of up to 40,000 participants internationally, including Pakistan, Saudi Arabia and Mexico (NCT04526990). Outcomes: A single dose of Ad5-nCoV protected against upper respiratory infection of SARS-CoV-2 in ferrets, according to a paper published 14 August in Nature Communications. Results from a Phase 1 trial show a humoral and immunogenic response to the vaccine, according to a paper published in The Lancet. Adverse reactions such as pain (54%), fever (46%), fatigue (44%), headache (39%), and muscle pain (17%) occurred in 83% of patients in the low and medium dose groups and 75% of patients in the high dose group. In the Phase 2 trial, neutralizing antibodies and specific interferon γ enzyme-linked immunospot assay responses were observed at all dose levels for most participants. Status: On 25 June, China’s Central Military Commission announced the military had been approved to use Ad5-nCoV for a period of 1 year, according to reporting in Reuters."X <- res$therapeutics
summary(X)## phase candidates
## 1 Phase 3 14
## 2 Phase 2/3 12
## 3 Phase 2 14
## 4 Phase 1/2/3 1
## 5 Phase 1b 4
## 6 Phase 2/3/4 1
## 7 Phase 2b/3 2
## 8 Phase 3/4 1
## 9 Phase 2/4 1
## 10 Phase 1/2 1
## 11 Phase 1 2
## 12 No longer being studied for COVID-19 1head(X["All"])## id medicationClass tradeName
## 1 id1 IL-6 receptor agonist Actemra (tocilizumab)
## 2 id2 Antirheumatic agent Bucillamine
## 3 id3 Monoclonal antibody Bamlanivimab (LY-CoV555)
## 4 id4 Monoclonal antibody VIR-7831 (GSK4182136)
## 5 id5 Monoclonal antibody Mavrilimumab
## 6 id6 Antibody cocktail Casirivimab/imdevimab (REGN-COV2)
## developerResearcher sponsors trialPhase lastUpdate
## 1 Roche Various Phase 3 2020-12-24
## 2 Revive Therapeutics Ltd. Revive Therapeutics Ltd. Phase 3 2020-12-24
## 3 Lilly Lilly Phase 2/3 2020-12-24
## 4 Vir Biotechnology, Inc. Vir Biotechnology, Inc. Phase 2/3 2020-12-24
## 5 Kiniksa Pharmaceuticals The Cleveland Clinic Phase 2 2020-12-24
## 6 Regeneron Regeneron Phase 1/2/3 2020-12-24X[ID="id1"] ## [1] "Background: Actemra is a indicated to treat autoimmune diseases such as rheumatoid arthritis as well as cytokine release syndrome. Research from China has shown Actemra may be an effective treatment for patients with severe cases of COVID-19. Trials: Actemra is being evaluated in the following high-profile trials: COVACTA (NCT04320615) and EMPACTA (NCT04372186). The Hôpitaux de Paris (CORIMUNO-19) is assessing Actemra in a trial for COVID-19 associated pneumonia (NCT04331808) in a Phase 2 trial. Outcomes: Evidence is beginning to point to Actemra having a beneficial outcome for COVID-19 patients in some, but not all, scenarios. Evidence for benefit: - Results from EMPACTA indicate Actemra reduced the need for mechanical ventilation in patients with COVID-19 associated pneumonia. In EMPACTA, 12.0% of patients receiving Actemra received mechanical ventilation compared with 19.3% of patients in the placebo group (P = .04); however, Actemra did not improve rates of survival, according to data published in the New England Journal of Medicine. - Preliminary results from CORIMUNO-19 showed Actemra “improves significantly clinical outcomes” of pneumonia associated with COVID-19. - The drug may also improve survival in patients with cytokine release syndrome, according to a study in CHEST. - Results from the University of Michigan published in the journal Clinical Infectious Diseases showed a 45% reduction in hazard of death for COVID-19 patients and improved status compared with patients who did not receive the drug. - In a multicenter cohort study of 4,485 adults with COVID-19 published in JAMA Internal Medicine, researchers found a lower risk of mortality in adults who received Actemra within 2 days of admission to the ICU compared with patients who did not receive Actemra as part of their care. Evidence showing mixed results: - Researchers on behalf of the Niguarda COVID-19 Working Group released a comparative analysis in the Journal of Infection that noted Actemra is potentially effective, but recommended caution when using the drug. - A randomized, double-blind, placebo-controlled trial published in the New England Journal of Medicine by researchers at Massachusetts General Hospital found Actemra was not effective in reducing need for intubation, disease progression, or death but left open the opportunity that the drug did carry some benefit due to wide confidence intervals in comparisons of efficacy. - CORIMUNO-19: There were a lower number of patients hospitalized with COVID-19 and moderate-to-severe pneumonia taking Actemra who required noninvasive ventilation, intubation, or died at 14 days compared with placebo, but Actemra did not meet the primary outcome of reducing clinical progression scores by 5 days after starting treatment. Evidence showing no benefit: - In the COVID-BioB Study, patients who received Actemra instead of standard of care had improved clinical outcomes (69% vs. 61%; P = .61) and reduced mortality (15% vs. 33%; P = .15), but neither result was statistically significant. - Results posted in medRxiv by researchers at the University of North Carolina, Chapel Hill, showed that of 11 patients with severe COVID-19 requiring ventilation, Actemra reduced C-reactive protein levels but did not result in significant improvement in temperature and oxygen requirements. - An Italian study sponsored by the Italian Medicine Agency (AIFA) was stopped after Actemra failed to perform better than standard of care in reducing respiratory symptoms, intensive care visits and mortality. - Roche also provided an update for COVACTA indicating the drug did not meet its primary or secondary endpoints of improved clinical status and reduced mortality. Status: COVACTA has been completed; EMPACTA and CORIMUNO-19 are active, but not recruiting."Visualization
We provide a visualization function as a redesign “plot”.
plot(
x,
region = "Global",
continuous_scale = FALSE,
palette = "Reds",
date = NULL,
from = NULL,
to = NULL,
title = "COVID-19",
type = "cases",
...
)Here, type could be one of “cases”,“deaths”,“recovered”,“active”,“todayCases”,“todayDeaths”,“todayRecovered”,“population” and “tests”. By default, color palette is “Reds”, more color palettes can be found here: palette
To get the overview for the latest status, the mini code required is as below:
X <- res$latest
plot(X)
Or To get the overview for the detection testing status,
plot(X, type="tests",palette="Green")
It could be also intuitively compare the number of new confirmed cases per day among different countries.
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionX <- res$historical
tmp <- X["global"] %>%
group_by(country) %>%
arrange(country,date) %>%
mutate(diff = cases - lag(cases, default = first(cases))) %>%
filter(country %in% c("Australia", "Japan", "Italy", "Germany", "China"))
ggplot(tmp,aes(date, log(diff+1), color=country)) + geom_line() +
labs(y="Log2(daily increase cases)") +
theme(axis.text = element_text(angle = 15, hjust = 1)) +
scale_x_date(date_labels = "%Y-%m-%d") +
theme_minimal()
user could also plot the outbreak map on the past time with historical data by specify a date in function plot().
Y <- res$historical
plot(Y, region="Global" ,date = "2020-08-01", type="cases")
Animations plot
Animated world-wide epidemic maps could be generated in the similar way. This is the example to draw a spread animation from 2020-03-01 to 2020-08-01, with little code.
library(nCov2019)
res = query()
from = "2020-03-01"
to = "2020-08-01"
y = res$historical
plot(y, from = from, to=to)
Other plots
If you wanted to visualize the cumulative summary data, an example plot could be the following:
library(ggplot2)
x <- res$historical
d = x['Japan' ] # you can replace Anhui with any province
d = d[order(d$cases), ]
ggplot(d,
aes(date, cases)) +
geom_col(fill = 'firebrick') +
theme_minimal(base_size = 14) +
xlab(NULL) + ylab(NULL) +
scale_x_date(date_labels = "%Y/%m/%d") +
labs(caption = paste("accessed date:", max(d$date)))
Plot the trend for for the Top 10 increase cases countries on last day
library("dplyr")
library("ggrepel")
x <- res$latest
y <- res$historical
country_list = x["global"]$country[1:10]
y[country_list] %>%
subset( date > as.Date("2020-10-01") ) %>%
group_by(country) %>%
arrange(country,date) %>%
mutate(increase = cases - lag(cases, default = first(cases))) -> df
ggplot(df, aes(x=date, y=increase, color=country ))+
geom_smooth() +
geom_label_repel(aes(label = paste(country,increase)),
data = df[df$date == max(df$date), ], hjust = 1) +
labs(x=NULL,y=NULL)+
theme_bw() + theme(legend.position = 'none') ## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Plot the curve of cases, recovered and deaths for specify country
library('tidyr')
library('ggrepel')
library('ggplot2')
y <- res$historical
country = "India"
y[country] -> d
d <- gather(d, curve, count, -date, -country)
ggplot(d, aes(date, count, color = curve)) + geom_point() + geom_line() +
labs(x=NULL,y=NULL,title=paste("Trend of cases, recovered and deaths in", country)) +
scale_color_manual(values=c("#f39c12", "#dd4b39", "#00a65a")) +
theme_bw() +
geom_label_repel(aes(label = paste(curve,count)),
data = d[d$date == max(d$date), ], hjust = 1) +
theme(legend.position = "none",
axis.text = element_text(angle = 15, hjust = 1)) +
scale_x_date(date_labels = "%Y-%m-%d")
Heatmap for cases per country
Here is the example code for draw a heatmap for the historical data range in nCov2019.
library('tidyr')
library('ggrepel')
library('ggplot2')
y <- res$historical
d <- y["global"]
d <- d[d$cases > 0,]
length(unique(d$country))## [1] 192d <- subset(d,date <= as.Date("2020-3-19"))
max_time <- max(d$date)
min_time <- max_time - 7
d <- d[d$date >= min_time,]
dd <- d[d$date == max(d$date,na.rm = TRUE),]
d$country <- factor(d$country,
levels=unique(dd$country[order(dd$cases)]))
breaks = c(0,1000, 10000, 100000, 10000000)
ggplot(d, aes(date, country)) +
geom_tile(aes(fill = cases), color = 'black') +
scale_fill_viridis_c(trans = 'log', breaks = breaks,
labels = breaks) +
xlab(NULL) + ylab(NULL) +
scale_x_date(date_labels = "%Y-%m-%d") + theme_minimal()
Plot the global trend in a novel way.
require(dplyr)
y <- res$historical
d <- y["global"]
time = as.Date("2020-03-19")
dd <- filter(d, date == time) %>%
arrange(desc(cases))
dd = dd[1:40, ]
dd$country = factor(dd$country, levels=dd$country)
dd$angle = 1:40 * 360/40
require(ggplot2)
p <- ggplot(dd, aes(country, cases, fill=cases)) +
geom_col(width=1, color='grey90') +
geom_col(aes(y=I(5)), width=1, fill='grey90', alpha = .2) +
geom_col(aes(y=I(3)), width=1, fill='grey90', alpha = .2) +
geom_col(aes(y=I(2)), width=1, fill = "white") +
scale_y_log10() +
scale_fill_gradientn(colors=c("darkgreen", "green", "orange", "firebrick","red"), trans="log") +
geom_text(aes(label=paste(country, cases, sep="\n"),
y = cases *.8, angle=angle),
data=function(d) d[d$cases > 700,],
size=3, color = "white", fontface="bold", vjust=1) +
geom_text(aes(label=paste0(cases, " cases ", country),
y = max(cases) * 2, angle=angle+90),
data=function(d) d[d$cases < 700,],
size=3, vjust=0) +
coord_polar(direction=-1) +
theme_void() +
theme(legend.position="none") +
ggtitle("COVID19 global trend", time)
p
Number of days since 1 million cases per country
require(dplyr)
require(ggplot2)
require(shadowtext)## Loading required package: shadowtexty <- res$historical
d <- y["global"]
dd <- d %>%
as_tibble %>%
filter(cases > 1000000) %>%
group_by(country) %>%
mutate(days_since_1m = as.numeric(date - min(date))) %>%
ungroup
breaks=c(1000, 10000, 20000, 50000, 500000,500000,5000000,20000000)
p <- ggplot(dd, aes(days_since_1m, cases, color = country)) +
geom_smooth(method='lm', aes(group=1),
data = dd,
color='grey10', linetype='dashed') +
geom_line(size = 0.8) +
geom_point(pch = 21, size = 1) +
scale_y_log10(expand = expansion(add = c(0,0.1)),
breaks = breaks, labels = breaks) +
scale_x_continuous(expand = expansion(add = c(0,1))) +
theme_minimal(base_size = 14) +
theme(
panel.grid.minor = element_blank(),
legend.position = "none",
plot.margin = margin(3,15,3,3,"mm")
) +
coord_cartesian(clip = "off") +
geom_shadowtext(aes(label = paste0(" ",country)), hjust=0, vjust = 0,
data = . %>% group_by(country) %>% top_n(1,days_since_1m),
bg.color = "white") +
labs(x = "Number of days since 1,000,000th case", y = "",
subtitle = "Total number of cases")
print(p)## `geom_smooth()` using formula 'y ~ x'
Dashboard
dashboard could launch as below:
dashboard()
statistic item explanation
| statistic | explain |
|---|---|
| active | active number = comfirmed cases - deaths - recoveredd |
| activePerOneMillion | active number / million population |
| cases | comfirmed cases |
| casesPerOneMillion | comfirmed cases / million population |
| continent | continent |
| country | country |
| critical | Critical patients |
| criticalPerOneMillion | Critical patients / million population |
| date | date |
| deaths | deaths |
| deathsPerOneMillion | deaths patients / million population |
| oneCasePerPeople | oneCasePerPeople |
| oneDeathPerPeople | oneDeathPerPeople |
| oneTestPerPeople | oneTestPerPeople |
| population | population |
| recovered | recovered |
| recoveredPerOneMillion | recoveredPerOneMillion |
| tests | COVID-19 test |
| testsPerOneMillion | COVID-19 test / million population |
| todayCases | comfirm cases in today |
| todayDeaths | comfirm cases in today |
| todayRecovered | comfirm cases in today |
| updated | the latest update time |
Session Info
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin19.5.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS/LAPACK: /usr/local/Cellar/openblas/0.3.10_1/lib/libopenblasp-r0.3.10.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] shadowtext_0.0.7 tidyr_1.1.2 ggrepel_0.8.2 dplyr_1.0.2
## [5] ggplot2_3.3.2 nCov2019_0.4.2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 RColorBrewer_1.1-2 pillar_1.4.6 compiler_4.0.2
## [5] plyr_1.8.6 prettydoc_0.4.1 tools_4.0.2 digest_0.6.25
## [9] viridisLite_0.3.0 lattice_0.20-41 nlme_3.1-148 jsonlite_1.7.1
## [13] evaluate_0.14 lifecycle_0.2.0 tibble_3.0.3 gtable_0.3.0
## [17] mgcv_1.8-31 pkgconfig_2.0.3 rlang_0.4.7 Matrix_1.2-18
## [21] yaml_2.2.1 xfun_0.20 withr_2.2.0 downloader_0.4
## [25] stringr_1.4.0 knitr_1.30 generics_0.0.2 vctrs_0.3.4
## [29] maps_3.3.0 grid_4.0.2 tidyselect_1.1.0 glue_1.4.2
## [33] R6_2.4.1 rmarkdown_2.3 farver_2.0.3 purrr_0.3.4
## [37] reshape2_1.4.4 magrittr_1.5 splines_4.0.2 scales_1.1.1
## [41] ellipsis_0.3.1 htmltools_0.5.0 colorspace_1.4-1 labeling_0.3
## [45] stringi_1.5.3 munsell_0.5.0 crayon_1.3.4